Utility-Aware Optimal Resource Allocation Protocol for UAV-Assisted Small Cells With Heterogeneous Coverage Demands

Abstract

In this paper, we consider a UAV-assisted smallcell having heterogeneous users with different data rate and coverage demands. Specifically, we propose a novel utility-aware resource-allocation protocol to maximize the utility of UAV by allowing it to simultaneously serve the highest possible number of heterogeneous users with available energy resources. In this regard, first we derive a closed-form expression for rate-coverage probability of a user considering Rician fading to incorporate the strong line of sight (LoS) component in UAV communication. Next since this UAV utility maximization problem is non-convex and combinatorial, to obtain the global optimal resource allocation policy we propose an iterative feasibility checking method for fixed integers ranging from lower to upper bound on the number of users that can be served by UAV. To further reduce the complexity, we formulate an equivalent problem aimed at minimizing per user energy consumption, where tight analytical relaxation on rate-coverage probability constraint is used along with semiclosed expressions for joint-optimal power and time allocation. Lastly, via detailed numerical investigation, we validate our analytical claims, present insights on the impact of key system parameters, and demonstrate that 60% more users can be served using the proposed scheme as compared to relevant benchmarks.

UAV-Aided Wireless Power Transfer and Data Collection in Rician Fading

“…As Rayleigh fading model is only applicable to the cases when there is no LoS link from the transmitter to the receiver, and the air-to-ground (A2G) channels are often dominated by LoS links, more and more recent works started to investigate the performance of UAV-aided communications with Rician fading model, which comprises a deterministic LoS component with a random multipath component, and is suitable for characterizing A2G channels with less shadowing but non-negligible small-scale fading. In [19], the number of users that could be served by the UAV was maximized in Rician fading, under the constraint of users' minimum rate-coverage probability requirements. In [20], the outage probability of the UAV-aided data collection with WPT was minimized in Rician fading, where the fixed UAV's altitude was assumed.…”

“…However, in aforementioned works, see e.g., [19]- [20], the Rician factor was assumed as a constant without considering the effect of elevation angle on the channel gain. As UAV's elevation angle has great impact on the A2G link quality, it has been reported that omitting the effect of the elevation angle on the Rician channel modeling causes the analyzing bias to practical scenarios [21]- [23].…”

“…To fill this gap, this paper studies the outage and coverage performance of UAV-aided data collection with WPT for wireless powered network in Rician fading channels, where in order to take the effect of UAV's elevation angle into account, the angle-dependent Rician factor and angle-dependent path loss exponent are adopted. It is noticed that in [19]- [24], it was assumed that the SN could always perform data uploading no matter how much energy was harvested. That is, the information transmission outage was assumed to occur only in the information transmission stage.…”

“…Proof: The proof can be found in Appendix C. It is observed that, due to the presence of highly non-linear terms in (19), it is not possible to obtain the explicit analytic solution for θ s,c . Thus, the GS-based linear search technique [34] is adopted to find the critical point θ s,c by numerically solving (19). For clarity, the presented GS-based method is summarized in Algorithm 1, where the function f θ used in the method is defined as…”

“…(23) Proof: The proof can be found in Appendix D. Similar to (19), there is no explicit analytic expression for ρ * . Thus, the GS-based method in Algorithm 1 also can be adopted to find the optimal ρ * by numerically solving (23).…”